




"""simple docstring"""




from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
					import PIL


lowerCAmelCase				:     Dict					=				logging.get_logger(__name__)



def        a__					(							snake_case__							)		->   List[List[ImageInput]]:
			if isinstance(snake_case__			,					(list, tuple)							) and isinstance(videos[0]			,					(list, tuple)							) and is_valid_image(videos[0][0]							):
						return videos

			elif isinstance(snake_case__			,					(list, tuple)							) and is_valid_image(videos[0]							):
						return [videos]

			elif is_valid_image(snake_case__							):
						return [[videos]]

			raise ValueError(F'Could not make batched video from {videos}'							)






class        __magic_name__				(			UpperCAmelCase__							):






				'''simple docstring'''
				__UpperCamelCase         =		["pixel_values"]





				def __init__(			self						,					_a = True						,					_a = None						,					_a = PILImageResampling.BILINEAR						,					_a = True						,					_a = None						,					_a = True						,					_a = 1 / 255						,					_a = True						,					_a = None						,					_a = None						,					**_a						,					):





							"""simple docstring"""
							super().__init__(**_a					)
							lowerCamelCase      =   size if size is not None else {"""shortest_edge""": 224}
							lowerCamelCase      =   get_size_dict(_a						,					default_to_square=_a					)
							lowerCamelCase      =   crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
							lowerCamelCase      =   get_size_dict(_a						,					param_name="""crop_size"""					)

							lowerCamelCase      =   do_resize
							lowerCamelCase      =   size
							lowerCamelCase      =   do_center_crop
							lowerCamelCase      =   crop_size
							lowerCamelCase      =   resample
							lowerCamelCase      =   do_rescale
							lowerCamelCase      =   rescale_factor
							lowerCamelCase      =   do_normalize
							lowerCamelCase      =   image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
							lowerCamelCase      =   image_std if image_std is not None else IMAGENET_STANDARD_STD





				def   _lowerCAmelCase							(			self						,					_a						,					_a						,					_a = PILImageResampling.BILINEAR						,					_a = None						,					**_a						,					):





							"""simple docstring"""
							lowerCamelCase      =   get_size_dict(_a						,					default_to_square=_a					)
							if "shortest_edge" in size:
										lowerCamelCase      =   get_resize_output_image_size(_a						,					size["""shortest_edge"""]						,					default_to_square=_a					)
							elif "height" in size and "width" in size:
										lowerCamelCase      =   (size["""height"""], size["""width"""])
							else:
										raise ValueError(f'Size must have \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}'					)
							return resize(_a						,					size=_a						,					resample=_a						,					data_format=_a						,					**_a					)





				def   _lowerCAmelCase							(			self						,					_a						,					_a						,					_a = None						,					**_a						,					):





							"""simple docstring"""
							lowerCamelCase      =   get_size_dict(_a					)
							if "height" not in size or "width" not in size:
										raise ValueError(f'Size must have \'height\' and \'width\' as keys. Got {size.keys()}'					)
							return center_crop(_a						,					size=(size["""height"""], size["""width"""])						,					data_format=_a						,					**_a					)





				def   _lowerCAmelCase							(			self						,					_a						,					_a						,					_a = None						,					**_a						,					):





							"""simple docstring"""
							return rescale(_a						,					scale=_a						,					data_format=_a						,					**_a					)





				def   _lowerCAmelCase							(			self						,					_a						,					_a						,					_a						,					_a = None						,					**_a						,					):





							"""simple docstring"""
							return normalize(_a						,					mean=_a						,					std=_a						,					data_format=_a						,					**_a					)





				def   _lowerCAmelCase							(			self						,					_a						,					_a = None						,					_a = None						,					_a = None						,					_a = None						,					_a = None						,					_a = None						,					_a = None						,					_a = None						,					_a = None						,					_a = None						,					_a = ChannelDimension.FIRST						,					):





							"""simple docstring"""
							if do_resize and size is None or resample is None:
										raise ValueError("""Size and resample must be specified if do_resize is True."""					)

							if do_center_crop and crop_size is None:
										raise ValueError("""Crop size must be specified if do_center_crop is True."""					)

							if do_rescale and rescale_factor is None:
										raise ValueError("""Rescale factor must be specified if do_rescale is True."""					)

							if do_normalize and (image_mean is None or image_std is None):
										raise ValueError("""Image mean and std must be specified if do_normalize is True."""					)

							# All transformations expect numpy arrays.
							lowerCamelCase      =   to_numpy_array(_a					)

							if do_resize:
										lowerCamelCase      =   self.resize(image=_a						,					size=_a						,					resample=_a					)

							if do_center_crop:
										lowerCamelCase      =   self.center_crop(_a						,					size=_a					)

							if do_rescale:
										lowerCamelCase      =   self.rescale(image=_a						,					scale=_a					)

							if do_normalize:
										lowerCamelCase      =   self.normalize(image=_a						,					mean=_a						,					std=_a					)

							lowerCamelCase      =   to_channel_dimension_format(_a						,					_a					)
							return image





				def   _lowerCAmelCase							(			self						,					_a						,					_a = None						,					_a = None						,					_a = None						,					_a = None						,					_a = None						,					_a = None						,					_a = None						,					_a = None						,					_a = None						,					_a = None						,					_a = None						,					_a = ChannelDimension.FIRST						,					**_a						,					):





							"""simple docstring"""
							lowerCamelCase      =   do_resize if do_resize is not None else self.do_resize
							lowerCamelCase      =   resample if resample is not None else self.resample
							lowerCamelCase      =   do_center_crop if do_center_crop is not None else self.do_center_crop
							lowerCamelCase      =   do_rescale if do_rescale is not None else self.do_rescale
							lowerCamelCase      =   rescale_factor if rescale_factor is not None else self.rescale_factor
							lowerCamelCase      =   do_normalize if do_normalize is not None else self.do_normalize
							lowerCamelCase      =   image_mean if image_mean is not None else self.image_mean
							lowerCamelCase      =   image_std if image_std is not None else self.image_std

							lowerCamelCase      =   size if size is not None else self.size
							lowerCamelCase      =   get_size_dict(_a						,					default_to_square=_a					)
							lowerCamelCase      =   crop_size if crop_size is not None else self.crop_size
							lowerCamelCase      =   get_size_dict(_a						,					param_name="""crop_size"""					)

							if not valid_images(_a					):
										raise ValueError(
										    """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
										    """torch.Tensor, tf.Tensor or jax.ndarray."""					)

							lowerCamelCase      =   make_batched(_a					)

							lowerCamelCase      =   [
							    [
							        self._preprocess_image(
							            image=_a						,					do_resize=_a						,					size=_a						,					resample=_a						,					do_center_crop=_a						,					crop_size=_a						,					do_rescale=_a						,					rescale_factor=_a						,					do_normalize=_a						,					image_mean=_a						,					image_std=_a						,					data_format=_a						,					)
							        for img in video
							    ]
							    for video in videos
							]

							lowerCamelCase      =   {"""pixel_values""": videos}
							return BatchFeature(data=_a						,					tensor_type=_a					)









"""simple docstring"""




import os
from collections import namedtuple

import pytest

from datasets import ClassLabel, Features, Sequence, Value
from datasets.commands.test import TestCommand
from datasets.info import DatasetInfo, DatasetInfosDict


lowerCAmelCase				:     Dict					=				namedtuple(
    """_TestCommandArgs""",
    [
        """dataset""",
        """name""",
        """cache_dir""",
        """data_dir""",
        """all_configs""",
        """save_infos""",
        """ignore_verifications""",
        """force_redownload""",
        """clear_cache""",
    ],
    defaults=[None, None, None, False, False, False, False, False],
)



def        a__					(							snake_case__			,					snake_case__							)		->   Optional[Any]:
			return (abs(source - target							) / target) < 0.01



@pytest.mark.integration
def        a__					(							snake_case__							)		->   int:
			lowerCamelCase      =   _TestCommandArgs(dataset=snake_case__			,					all_configs=snake_case__			,					save_infos=snake_case__							)
			lowerCamelCase      =   TestCommand(*snake_case__							)
			test_command.run()
			lowerCamelCase      =   os.path.join(snake_case__			,					"""README.md"""							)
			assert os.path.exists(snake_case__							)
			lowerCamelCase      =   DatasetInfosDict.from_directory(snake_case__							)
			lowerCamelCase      =   DatasetInfosDict(
			    {
			        """default""": DatasetInfo(
			            features=Features(
			                {
			                    """tokens""": Sequence(Value("""string"""							)							),
			                    """ner_tags""": Sequence(
			                        ClassLabel(names=["""O""", """B-PER""", """I-PER""", """B-ORG""", """I-ORG""", """B-LOC""", """I-LOC"""]							)							),
			                    """langs""": Sequence(Value("""string"""							)							),
			                    """spans""": Sequence(Value("""string"""							)							),
			                }							)			,					splits=[
			                {
			                    """name""": """train""",
			                    """num_bytes""": 2_35_15_63,
			                    """num_examples""": 1_00_00,
			                },
			                {
			                    """name""": """validation""",
			                    """num_bytes""": 23_84_18,
			                    """num_examples""": 10_00,
			                },
			            ]			,					download_size=3_94_06_80			,					dataset_size=2_58_99_81			,					)
			    }							)
			assert dataset_infos.keys() == expected_dataset_infos.keys()
			for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
						lowerCamelCase ,  lowerCamelCase      =   getattr(dataset_infos["""default"""]			,					snake_case__							), getattr(expected_dataset_infos["""default"""]			,					snake_case__							)
						if key == "num_bytes":
									assert is_apercent_close(snake_case__			,					snake_case__							)
						elif key == "splits":
									assert list(snake_case__							) == list(snake_case__							)
									for split in result:
												assert result[split].name == expected[split].name
												assert result[split].num_examples == expected[split].num_examples
												assert is_apercent_close(result[split].num_bytes			,					expected[split].num_bytes							)
						else:
									result == expected



